Improve model card: Add metadata, paper link, and usage example

This PR enhances the model card for **TriP-LLM** by adding crucial metadata, including:
- `pipeline_tag`: `time-series-forecasting`
- `library_name`: `pytorch`
- `license`: `other` (as no specific license was provided in the context)
- Additional `tags`: `time-series`, `anomaly-detection`, `LLM`
- `datasets`: `NIPS-TS-SWAN`, `MSL`, `SMD`, `SWaT`, `PSM`

Furthermore, a direct link to the Hugging Face paper page ([TriP-LLM: A Tri-Branch Patch-wise Large Language Model Framework for Time-Series Anomaly Detection](https://huggingface.co/papers/2508.00047)) has been added.

The "Usage" section has been updated to include installation steps and a sample code snippet for reproducing the main experiments, as found in the GitHub README, to provide users with immediate instructions on how to run the model.

README.md

@@ -1,32 +1,70 @@
-# TriP-LLM
-
-This is the official checkpoints release for the  **TriP-LLM**, a novel framework for unsupervised anomaly detection in multivariate time-series data using pretrained Large Language Models (LLMs).
-
-## Model Description
-- **Name**: TriP-LLM  
-- **Task**: Time-Series Anomaly Detection  
-- **Framework**: PyTorch 
-- **Repository**: [GitHub – YYZStart/TriP-LLM](https://github.com/YYZStart/TriP-LLM)
-
-
-## Usage
-
-Please refer to our [GitHub repository](https://github.com/YYZStart/TriP-LLM)  
-for model definitions, training code, and usage examples.
-
-
-## 📎 Citation
-
-If you find this repository useful for your research, please cite our paper:
-
-```bibtex
-@misc{TriPLLM,
-      title={TriP-LLM: A Tri-Branch Patch-wise Large Language Model Framework for Time-Series Anomaly Detection}, 
-      author={Yuan-Cheng Yu and Yen-Chieh Ouyang and Chun-An Lin},
-      year={2025},
-      eprint={2508.00047},
-      archivePrefix={arXiv},
-      primaryClass={cs.LG},
-      url={https://arxiv.org/abs/2508.00047}, 
-}
+---
+license: other
+pipeline_tag: time-series-forecasting
+library_name: pytorch
+tags:
+- time-series
+- anomaly-detection
+- LLM
+datasets:
+- NIPS-TS-SWAN
+- MSL
+- SMD
+- SWaT
+- PSM
+---
+
+# TriP-LLM
+
+This is the official checkpoints release for the **TriP-LLM**, a novel framework for unsupervised anomaly detection in multivariate time-series data using pretrained Large Language Models (LLMs).
+
+The model was presented in the paper: [TriP-LLM: A Tri-Branch Patch-wise Large Language Model Framework for Time-Series Anomaly Detection](https://huggingface.co/papers/2508.00047)
+
+## Model Description
+- **Name**: TriP-LLM  
+- **Task**: Time-Series Anomaly Detection  
+- **Framework**: PyTorch 
+- **Repository**: [GitHub – YYZStart/TriP-LLM](https://github.com/YYZStart/TriP-LLM)
+
+
+## Usage
+
+To get started with TriP-LLM, you can follow the installation and usage instructions from the [GitHub repository](https://github.com/YYZStart/TriP-LLM).
+
+### Installation
+
+We conducted our experiments using PyTorch 2.4.1, Python 3.11 and CUDA 12.4.
+```bash
+pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu124
+```
+To install the required dependencies and set up the environment, run the following commands:
+
+```bash
+git clone https://github.com/YYZStart/TriP-LLM.git
+cd TriP-LLM
+pip install -r requirements.txt
+```
+
+### Reproduce Experiments
+
+You can reproduce our main experiments with:
+
+```bash
+python main.py
+```
+
+## 📎 Citation
+
+If you find this repository useful for your research, please cite our paper:
+
+```bibtex
+@misc{TriPLLM,
+      title={TriP-LLM: A Tri-Branch Patch-wise Large Language Model Framework for Time-Series Anomaly Detection}, 
+      author={Yuan-Cheng Yu and Yen-Chieh Ouyang and Chun-An Lin},
+      year={2025},
+      eprint={2508.00047},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2508.00047}, 
+}
 ```